Step counting method and device

ABSTRACT

The present invention provides a step counting method and device. The method comprises the following steps performed repeatedly: a) obtaining three monoaxial acceleration signals with a predetermined length from triaxial output of a triaxial acceleration sensor worn on a walkrunner; b) performing high-pass filtering on each obtained monoaxial acceleration signal; c) performing pitch detection on each high-pass filtered monoaxial acceleration signal; d) using the pitch obtained in each pitch detection as a cut-off frequency to set a low-pass or band-pass filter, and performing low-pass or band-pass filtering on corresponding high-pass filtered monoaxial acceleration signal by using it; e) obtaining acceleration signal extreme value points from each low-pass or band-pass filtered monoaxial acceleration signal and removing interfering extreme value points therein; f) counting the number of the acceleration signal extreme value points after the interfering extreme value points have been removed; g) determining the accumulative walkrun step number of the walkrunner. The method can count steps accurately.

FIELD OF THE INVENTION

The present invention relates to the field of sports equipment, inparticular, relates to a step counting method and device.

BACKGROUND OF THE INVENTION

Pedometer is a device capable of calculating the walking or running(hereinafter brief as walkrun) step number of its wearer. With more andmore concern of the health condition of human beings, the pedometerbecomes an aid for customizing sports schemes quantitatively, and hasbeen widely used.

Currently, the pedometer is mainly divided into two types, mechanicalpedometer and electronic pedometer. The mechanical pedometer generateselectrical pulses through the vibration of internal reed or elastic ballof the pedometer caused by its wearer's walkrun, and counts the numberof these electrical pulses through an internal processor, thusimplementing the function of step counting. The cost of mechanicalpedometer is relatively low, but the accuracy and sensitivity arepoorer. The electronic pedometer is generally based on the output signalof an acceleration sensor to obtain its wearer's walkrun step number.The electronic pedometer is low in power consumption, and both itsaccuracy and sensitivity are superior to the mechanical pedometer.Therefore, the electronic pedometer becomes a hotspot in the currentstudy of pedometer.

Human walkrun is a process with quasi-periodicity, therefore, althoughthe accelerations generated in each direction during human walkrun aredifferent, they all have the same quasi-periodicity, which is reflectedby that the accelerations in different directions contain the samepitch. The pedometer based on acceleration sensor can produce anoscillatory acceleration signal during its wearer's walkrun and analyzethe acceleration signal, to obtain the walkrun step number of itswearer. Specifically, the current pedometer based on acceleration sensordetermines its wearer's walkrun step number according to the number ofpeaks of oscillatory acceleration signal it produces. The disadvantageof the step counting methods of these pedometers is in that the directuse of peaks of oscillatory acceleration signal to determine the walkrunstep number can cause poor step counting accuracy, which will affect theimplementation of sports scheme of the pedometer's wearer.

SUMMARY OF THE INVENTION

The present invention is made for solving the problem existing in theaforesaid prior art. One object is for providing a step counting methodand device, and the step counting method and device can count walkrunstep number of the pedometer's wearer more accurately.

For achieving the aforesaid object, in one aspect of the presentinvention, a step counting method is provided, comprising the followingsteps performed repeatedly:

a) obtaining three monoaxial acceleration signals with a predeterminedlength from triaxial output of a triaxial acceleration sensor worn on awalkrunner;

b) performing high-pass filtering on each obtained monoaxialacceleration signal;

c) performing pitch detection on each high-pass filtered monoaxialacceleration signal, to obtain the pitch of each monoaxial accelerationsignal;

d) selecting the lowest pitch in the three monoaxial accelerationsignals as a cut-off frequency to set a low-pass or band-pass filter,and performing low-pass or band-pass filtering on each high-passfiltered monoaxial acceleration signal by using the low-pass orband-pass filter;

e) obtaining acceleration signal extreme value points from each low-passor band-pass filtered monoaxial acceleration signal and removinginterfering extreme value points therein;

f) counting the number of the acceleration signal extreme value pointsof each low-pass or band-pass filtered monoaxial acceleration signalafter the interfering extreme value points have been removed;

g) determining the walkrun step number obtained from this round of stepcounting based on the number of the acceleration signal extreme valuepoints in three monoaxial acceleration signals with the interferingextreme value points removed as counted in step f), and calculating theaccumulative walkrun step number of the walkrunner.

One or more methods of autocorrelation function method, cepstrum method,linear predictive coding method, and average magnitude differencefunction method can be used in the pitch detection. Preferably,performing pitch detection on the high-pass filtered monoaxialacceleration signal can comprise:

c2) calculating the autocorrelation function ρ(τ) of each high-passfiltered monoaxial acceleration signal by using the following formula:

${\rho (\tau)} = \frac{\sum\limits_{n = 0}^{N - 1}\; {{a(n)}{a\left( {n - \tau} \right)}}}{\sqrt{\sum\limits_{n = 0}^{N - 1}\; {{a^{2}(n)}{\sum\limits_{n = 0}^{N - 1}\; {a^{2}\left( {n - \tau} \right)}}}}}$

wherein, a(n) is the n-th value of each high-pass filtered monoaxialacceleration signal, N is the predetermined length of the signal, and0≦n<N, τ is a delay time, ρ(τ) is a normalized autocorrelation functionof the signal;

c3) obtaining the value of τ corresponding to the maximal value of ρ(τ),and the reciprocal of the τ value is the pitch of the signal.

Further preferably, before obtaining the autocorrelation function ρ(τ)of each high-pass filtered monoaxial acceleration signal, the method canfurther comprise: c1) performing attenuation process on the signal byusing a filter with which the attenuation of signal energy increasesprogressively from low frequency to high frequency.

Wherein, the removing the interfering extreme value points from theacceleration signal extreme value points comprises: filtering out theinterfering extreme value points from the acceleration signal extremevalue points through time gap; or, filtering out the interfering extremevalue points from the acceleration signal extreme value points throughtime gap and magnitude value.

Preferably, the interfering extreme value points may comprise such anacceleration signal extreme value point that the time gap between theacceleration signal extreme value point and the previous accelerationsignal extreme value point is smaller than a predetermined threshold.Or, the interfering extreme value points may comprise accelerationsignal extreme value points, whose magnitude values are not maximal,among each group of acceleration signal extreme value points in whichthe time gap between any two adjacent acceleration signal extreme valuepoints is smaller than a predetermined threshold.

Preferably, the step g) can comprise:

if the energy of each monoaxial acceleration signal not being markedlydifferent, averaging the number of the acceleration signal extreme valuepoints, with the interfering extreme value points removed, correspondingto each axis, and taking the average number as the walkrun step numberobtained in this round of step counting;

or, if the energy of each monoaxial acceleration signal being markedlydifferent, determining the walkrun step number obtained in this round ofstep counting based on the number of the acceleration signal extremevalue points, with the interfering extreme value points removed,corresponding to the monoaxial acceleration signal with the largestenergy.

Preferably, the step counting method can further comprise: calculatingdisplacement based on quadratic integral of at least one monoaxialacceleration signal for time.

According to another aspect of the present invention, a step countingdevice is provided, comprising:

a triaxial acceleration sensor;

a monoaxial acceleration signal obtaining unit, for obtaining threemonoaxial acceleration signals with a predetermined length from thetriaxial output of the triaxial acceleration sensor worn on awalkrunner;

a high-pass filtering unit, for performing high-pass filtering on eachmonoaxial acceleration signal obtained from the monoaxial accelerationsignal obtaining unit;

a pitch detecting unit, for performing pitch detection on each high-passfiltered monoaxial acceleration signal to obtain the pitch of eachmonoaxial acceleration signal;

a low-pass or band-pass filtering unit, for selecting the lowest pitchin the three monoaxial acceleration signals as a cut-off frequency toset a low-pass or band-pass filter, and performing low-pass or band-passfiltering on each high-pass filtered monoaxial acceleration signal byusing the low-pass or band-pass filter;

an extreme value point obtaining unit, for obtaining acceleration signalextreme value points from each low-pass or band-pass filtered monoaxialacceleration signal and removing interfering extreme value pointstherein;

a counting unit, for counting the number of the acceleration signalextreme value points of each low-pass or band-pass filtered monoaxialacceleration signal after the interfering extreme value points have beenremoved;

a step counting unit, for determining the walkrun step number obtainedfrom this round of step counting based on the number of the accelerationsignal extreme value points in three monoaxial acceleration signals withthe interfering extreme value points removed as counted by the countingunit, and calculating the accumulative walkrun step number of thewalkrunner.

Preferably, the pitch detecting unit can comprise:

an attenuation filter, for performing attenuation process on eachhigh-pass filtered monoaxial acceleration signal in such a manner thatattenuation degree increases from low frequency to high frequency;

a calculating unit, for calculating an autocorrelation function ρ(τ) ofthe signal output from the attenuation filter by using the followingformula:

${\rho (\tau)} = \frac{\sum\limits_{n = 0}^{N - 1}\; {{a(n)}{a\left( {n - \tau} \right)}}}{\sqrt{\sum\limits_{n = 0}^{N - 1}\; {{a^{2}(n)}{\sum\limits_{n = 0}^{N - 1}\; {a^{2}\left( {n - \tau} \right)}}}}}$

wherein, an) is the n-th value of the signal, N is the predeterminedlength of the signal, and 0≦n<N, τ is a delay time, ρ(τ) is a normalizedautocorrelation function of the signal;

a pitch obtaining unit, for obtaining the value of v corresponding tothe maximal value of ρ(τ), and outputting the reciprocal of the τ valueas the pitch of the high-pass filtered monoaxial acceleration signal.

Preferably, the step counting unit can comprise an acceleration signalenergy calculating unit, for calculating the energy of each of themonoaxial acceleration signals, and

if the energy of each monoaxial acceleration signal is not markedlydifferent, the step counting unit averages the number of theacceleration signal extreme value points, with the interfering extremevalue points removed, corresponding to each axis, and takes the averagenumber as the walkrun step number obtained in this round of stepcounting; or, if the energy of each monoaxial acceleration signal ismarkedly different, the step counting unit determines the walkrun stepnumber obtained in this round of step counting based on the number ofthe acceleration signal extreme value points, with the interferingextreme value points removed, corresponding to the monoaxialacceleration signal with the largest energy.

It can be known from the above description that, the step countingmethod and device of the present invention can better obtain pitchcomponent of three monoaxial acceleration signals output from thetriaxial acceleration sensor by performing high-pass filtering, low-passor band-pass filtering on these three monoaxial acceleration signals,and on such basis, can count the extreme value points corresponding towalkrun step number in the monoaxial acceleration signal more accuratelyby removing the interfering extreme value points, thus can perform stepcounting accurately, and facilitate the pedometer's wearer to monitorsports schemes accurately.

The aforesaid description is merely the summary of technical solution ofthe present invention, and for understanding the technical means of thepresent invention more clearly, it can be carried out according to thedisclosure of specification, and for making the aforesaid and otherobjectives, features and advantages of the present invention moreapparent to be understood, embodiments of the present invention areillustrated as follows.

BRIEF DESCRIPTION OF DRAWINGS

By reading the detailed description of the preferable embodimenthereinafter, all the other features and advantages will be apparent tothose skilled in the art. The attached drawings are merely for thepurpose of showing preferable embodiment, rather than deemed as alimitation of the present invention. Among the attached drawings:

FIG. 1 is a schematic drawing, showing an example of acceleration signalproduced by a triaxial acceleration sensor in three orientations duringwalkrun of its wearer;

FIG. 2 is a block diagram, showing a step counting method of oneembodiment of the present invention;

FIG. 3a is a signal graph, showing a representative normalized monoaxialacceleration signal with a predetermined length output from the triaxialacceleration sensor;

FIG. 3b is a signal graph, showing a high-pass filtered monoaxialacceleration signal;

FIG. 3c is a signal graph, showing a low-pass or band-pass filteredmonoaxial acceleration signal;

FIG. 3d is a signal graph, showing one example of extreme value point oflow-pass or band-pass filtered monoaxial acceleration signal;

FIG. 4 is a schematic spectral diagram of monoaxial acceleration signal;

FIG. 5 shows an example of frequency response curve of a filter withwhich the attenuation of signal energy increases progressively from lowfrequency to high frequency;

FIG. 6 is a signal graph, showing another example of extreme value pointof low-pass or band-pass filtered monoaxial acceleration signal;

FIG. 7 is a block diagram, showing a step counting device of oneembodiment of the present invention;

FIG. 8 schematically shows a block diagram of server for carrying outthe method according to the present invention; and

FIG. 9 schematically shows a storage unit for maintaining or carryingthe program codes for achieving the method according to the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention will be described in details in combination withthe attached drawings and specific embodiments.

In the following description, certain illustrative embodiments of thepresent invention are described only by illustration. Needless to say,one skilled in the art may recognize that, the embodiment can be amendedby using a variety of different ways without departing from the spiritand scope of the present invention. Accordingly, the attached drawingsand descriptions are illustrative in nature, and not intended to limitthe protection scope of the claims. In the present specification, thesame reference numerals denote the same or similar parts.

The step counting method of the present invention is suitable for stepcounting with a pedometer having a triaxial acceleration sensor. Thepedometer having the triaxial acceleration sensor produces oscillatoryacceleration signal with different magnitude values on each directionduring walkrun of its wearer. FIG. 1 is a schematic drawing, showing anexample of acceleration signal produced by the triaxial accelerationsensor in three directions during walkrun of its wearer, wherein,a_(x)/g, a_(y)/g, a_(z)/g are normalized acceleration signals producedby the triaxial acceleration sensor in x axis, y axis and z axis,respectively, g denotes acceleration of gravity. As shown in FIG. 1,a_(x)/g, a_(y)/g, a_(z)/g contain the same pitch although theirmagnitude values are different, and the pitch represents the reciprocalof motion period that both left foot and right foot of the pedometer'swearer move one step. In addition, a_(x)/g, a_(y)/g, a_(z)/g containdouble frequency component, which corresponds to the reciprocal ofmotion period that either left foot or right foot of the pedometer'swearer moves one step. In addition, the acceleration signal may furthercontain higher frequency component produced by other rhythmical motionof the body. Since the output of the triaxial acceleration sensor alsocontains high-frequency component and other noises besides the pitchcomponent, the step counting will not be accurate if the walkrun stepnumber is determined by directly searching the extreme value points ofthe acceleration signal. Therefore, the present invention provides astep counting method by processing the acceleration signal output by thetriaxial acceleration sensor to accurately obtain the extreme valuepoints corresponding to the pitch component in the acceleration signal,so as to obtain walkrun step number accurately.

FIG. 2 is a block diagram, showing a step counting method of oneembodiment of the present invention. As shown in FIG. 2, the stepcounting method of the embodiment of the present invention comprises thefollowing steps:

Firstly, in step S10, obtaining three monoaxial acceleration signalswith a predetermined length from triaxial output of a triaxialacceleration sensor worn on a walkrunner. FIG. 3a is a signal graph,showing the representative normalized monoaxial acceleration signal a/ghaving a predetermined length output from the triaxial accelerationsensor, wherein, a denotes acceleration, g denotes acceleration ofgravity. The predetermined length can be chosen according to actualsituation, and if the predetermined length is too long, it would not beeasy to obtain walkrun step number instantly, on the other hand, if thepredetermined length is too short, the accuracy of step counting woulddecrease. In the example of FIG. 3, the predetermined length is chosento be 3 seconds, while the present invention is not restricted thereto.

Subsequently, in step S20, performing high-pass filtering on eachobtained monoaxial acceleration signal. Since each monoaxialacceleration signal output from the triaxial acceleration sensorgenerally contains direct current (DC) component, and the existence ofthe DC component interferes with the analysis of each monoaxialacceleration signal, therefore, the DC component in the monoaxialacceleration signal is removed through high-pass filtering. FIG. 3b is asignal graph, showing a high-pass filtered monoaxial accelerationsignal. It can be seen from FIG. 3b that, after high-pass filtering, themonoaxial acceleration signal only contains alternative current (AC)component.

Subsequently, in step S30, performing pitch detection on each high-passfiltered monoaxial acceleration signal, to obtain the pitch of eachmonoaxial acceleration signal. As mentioned above, the monoaxialacceleration signal produced during walkrun may contain variousfrequency components corresponding to different rhythmical motions ofthe body, such as pitch component, double frequency component and otherhigh-frequency component. FIG. 4 is a schematic spectral diagram ofmonoaxial acceleration signal. Wherein, the pitch component is closelyrelated to walkrun step number, and it is more accurate to obtainwalkrun step number based on the pitch component. In order to obtain theacceleration signal only having the pitch component, it is necessary tofilter out the high-frequency components from the acceleration signal.And for filtering out the high-frequency components, it is required todetect the frequency of the pitch component roughly, to facilitateconstructing a suitable filter for filtering out the high-frequencycomponents beyond the pitch component.

There are various methods for pitch detection, e.g., conventionalmethods in voice signal pitch detection such as autocorrelation functionmethod, cepstrum method, linear predictive coding method, averagemagnitude difference function method can be used. Preferably,autocorrelation function method can be used.

Specifically, for each high-pass filtered monoaxial acceleration signal,firstly the autocorrelation function ρ(τ) of the signal is calculated byusing the following formula:

${\rho (\tau)} = \frac{\sum\limits_{n = 0}^{N - 1}\; {{a(n)}{a\left( {n - \tau} \right)}}}{\sqrt{\sum\limits_{n = 0}^{N - 1}\; {{a^{2}(n)}{\sum\limits_{n = 0}^{N - 1}\; {a^{2}\left( {n - \tau} \right)}}}}}$

wherein, a(n) is the n-th value of the signal, N is the predeterminedlength of the signal, and 0≦n<N, τ is a delay time, ρ(τ) is a normalizedautocorrelation function of the signal. Then, obtaining the value of τcorresponding to the maximal value of ρ(τ), and the reciprocal of the τvalue is the pitch of the signal.

In an actual monoaxial acceleration signal, other frequency components(e.g. double frequency component) besides the pitch sometimes haverather large energy, as shown in FIG. 4. Thus, when calculating themaximal value of ρ(τ) to obtain the value of τ corresponding to themaximal value, a rather big error may be produced. Therefore, in orderto obtain the pitch 1/τ accurately by using the autocorrelation functionmethod, before obtaining the autocorrelation function ρ(τ), it ispossible to selectively attenuate the monoaxial acceleration signalfirstly, to suppress the high-frequency components in the monoaxialacceleration signal, so as to highlight the pitch component in themonoaxial acceleration signal, and reduce the error of the obtainedpitch. In one embodiment of the present invention, the attenuationprocess on the monoaxial acceleration signal can be done by a filterwith which the attenuation of signal energy increases progressively fromlow frequency to high frequency. FIG. 5 shows an example of frequencyresponse curve of the filter with which the attenuation of signal energyincreases progressively from low frequency to high frequency. After themonoaxial acceleration signal is attenuated through the filter, thelow-frequency component in the signal is attenuated rather slightly,whereas the high-frequency component is attenuated rather greatly. Thus,when the pitch is calculated by further performing autocorrelationfunction method on the monoaxial acceleration signal having filteredthrough the filter, the obtained pitch is relatively accurate.

Subsequently, in step S40, selecting the lowest pitch in the threemonoaxial acceleration signals as a cut-off frequency to set a low-passor band-pass filter, and performing low-pass or band-pass filtering oneach high-pass filtered monoaxial acceleration signal by using thelow-pass or band-pass filter. After low-pass or band-pass filtering, itis possible to obtain a relatively smooth signal, so as to facilitateaccurately counting the extreme value points of the acceleration signalscorresponding to the walkrun step number. FIG. 3c is a signal graph,showing a low-pass or band-pass filtered monoaxial acceleration signal.

Subsequently, in step S50, obtaining acceleration signal extreme valuepoints from each low-pass or band-pass filtered monoaxial accelerationsignal and removing interfering extreme value points therein. FIG. 3d isa signal graph, showing one example of extreme value points of low-passor band-pass filtered monoaxial acceleration signal, wherein,symbol+denotes the extreme value points (including maximal and minimalvalue points). FIG. 3d demonstrates a rather specific example, wherein,noise interference in the low-pass or band-pass filtered monoaxialacceleration signal is almost non-existent. In a more general condition,after low-pass or band-pass filtering, there still exists noiseinterference in the monoaxial acceleration signal, which is manifestedas the existence of interfering extreme value points. FIG. 6 is a signalgraph, showing another example of extreme value points of low-pass orband-pass filtered monoaxial acceleration signal. As shown in FIG. 6,interfering extreme value points (as indicated by arrow in FIG. 6)exists in the low-pass or band-pass filtered monoaxial accelerationsignal, these interfering extreme value points do not represent theextreme value points related to periodic motion, which only result inover-counting of step number, and removing these interfering extremevalue points can make the counted step number more accurate. Thus, theseinterfering extreme value points need to be removed so as to obtain theextreme value points corresponding to walkrun step number accurately.

In fact, the walkrun step number only corresponds to the number ofextreme value points in the monoaxial acceleration signal, and is notclosely related to the exact positions of these extreme value points, inother words, it is enough to remove appropriate number of extreme valuepoints for ensuring that the motion period of both left foot and rightfoot moving one step corresponds to an extreme value point. Therefore,the method for removing interfering extreme value point may not beunique.

In one embodiment of the present invention, the interfering extremevalue points may comprise such an acceleration signal extreme valuepoint that the time gap between this acceleration signal extreme valuepoint and the previous acceleration signal extreme value point issmaller than a predetermined threshold, wherein, the predeterminedthreshold is far smaller than the period of the pitch component of themonoaxial acceleration signal. In this embodiment, in each group ofextreme value points positioning closer, only the leftmost extreme valuepoint is kept, while the other extreme value points are removed asinterfering extreme value points. In this manner, according to the timegap between acceleration signal extreme value points, the interferingextreme value points are filtered out from the acceleration signalextreme value points.

In another embodiment of the present invention, the interfering extremevalue points may comprise acceleration signal extreme value points,whose magnitude values are not maximal, among each group of accelerationsignal extreme value points in which the time gap between any twoadjacent acceleration signal extreme value points is smaller than apredetermined threshold. In other words, in this embodiment, in eachgroup of extreme value points positioning closer, only the accelerationsignal extreme value point with maximal magnitude value is kept, whilethe other extreme value points are removed as interfering extreme valuepoints. In this manner, according to the time gap between accelerationsignal extreme value points and the magnitude values of accelerationsignal extreme value points, the interfering extreme value points arefiltered out from the acceleration signal extreme value points.

Subsequently, in step S60, counting the number of the accelerationsignal extreme value points of each low-pass or band-pass filteredmonoaxial acceleration signal after the interfering extreme value pointshave been removed.

Subsequently, in step S70, determining the walkrun step number obtainedfrom this round of step counting based on the number of the accelerationsignal extreme value points in three monoaxial acceleration signals withthe interfering extreme value points removed as counted in step S60, andcalculating the accumulative walkrun step number of the walkrunner.

For example, if the energy of each monoaxial acceleration signal is notmarkedly different (it can be determined whether the energy is notmarkedly different by setting a predetermined threshold), average thenumber of the acceleration signal extreme value points, with theinterfering extreme value points removed, corresponding to each axis,and the average number is taken as the walkrun step number obtained inthis round of step counting. For another example, if the energy of eachmonoaxial acceleration signal is markedly different (it can bedetermined whether the energy is markedly different by setting apredetermined threshold), the walkrun step number obtained in this roundof step counting can be determined based on the number of theacceleration signal extreme value points, with the interfering extremevalue points removed, corresponding to the monoaxial acceleration signalwith the largest energy.

Repeating the aforesaid steps S10-S70, the walkrun step number obtainedin each round of step counting is accumulated, thus a total accumulativestep number can be obtained.

In addition, in the aforesaid method, a displacement can be calculatedbased on quadratic integral of at least one monoaxial accelerationsignal for time, so as to provide a reference of actual motion distancefor the walkrunner. In addition, motion in situ or actual walkrun can bedistinguished according to the size of the displacement.

Hereinbefore the step counting method of the present invention isdescribed with reference to FIGS. 1-6. The step counting method of thepresent invention can be achieved through software, and can also beachieved through hardware, or achieved in combination with software andhardware.

FIG. 7 is a block diagram, showing a step counting device of oneembodiment of the present invention. As shown in FIG. 7, the stepcounting device 1000 comprises: a triaxial acceleration sensor 100, amonoaxial acceleration signal obtaining unit 200, a high-pass filteringunit 300, a pitch detecting unit 400, a low-pass or band-pass filteringunit 500, an extreme value point obtaining unit 600, a counting unit700, and a step counting unit 800.

The monoaxial acceleration signal obtaining unit 200 is for obtainingthree monoaxial acceleration signals with a predetermined length fromthe triaxial output of the triaxial acceleration sensor 100 worn on awalkrunner.

The high-pass filtering unit 300 is for performing high-pass filteringon each monoaxial acceleration signal obtained from the monoaxialacceleration signal obtaining unit 200.

The pitch detecting unit 400 is for performing pitch detection on eachhigh-pass filtered monoaxial acceleration signal to obtain the pitch ofeach monoaxial acceleration signal.

The low-pass or band-pass filtering unit 500 selects the lowest pitch ofthe three monoaxial acceleration signals as a cut-off frequency to set alow-pass or band-pass filter, and performs low-pass or band-passfiltering on each high-pass filtered monoaxial acceleration signal byusing the low-pass or band-pass filter.

The extreme value point obtaining unit 600 is for obtaining accelerationsignal extreme value points from each low-pass or band-pass filteredmonoaxial acceleration signal and removing interfering extreme valuepoints therein.

The counting unit 700 is for counting the number of the accelerationsignal extreme value points of each low-pass or band-pass filteredmonoaxial acceleration signal after interfering extreme value pointshave been removed.

The step counting unit 800 determines the walkrun step number obtainedfrom this round of step counting based on the number of the accelerationsignal extreme value points in three monoaxial acceleration signals withthe interfering extreme value points removed as counted by the countingunit 700, and calculates the accumulative walkrun step number of thewalkrunner.

Preferably, the pitch detecting unit 400 can comprise: an attenuationfilter, for performing attenuation process on each high-pass filteredmonoaxial acceleration signal in such a manner that attenuation degreeincreases progressively from low frequency to high frequency; acalculating unit, for calculating an autocorrelation function ρ(τ) ofthe signal output from the attenuation filter by using the followingformula:

${\rho (\tau)} = \frac{\sum\limits_{n = 0}^{N - 1}\; {{a(n)}{a\left( {n - \tau} \right)}}}{\sqrt{\sum\limits_{n = 0}^{N - 1}\; {{a^{2}(n)}{\sum\limits_{n = 0}^{N - 1}\; {a^{2}\left( {n - \tau} \right)}}}}}$

wherein, a(n) is the n-th value of the signal, N is the predeterminedlength of the signal, and 0≦n<N, τ is a delay time, ρ(τ) is a normalizedautocorrelation function of the signal; a pitch obtaining unit, forobtaining the value of τ corresponding to the maximal value of ρ(τ), andoutputting the reciprocal of the τ value as the pitch of the high-passfiltered monoaxial acceleration signal.

Preferably, the step counting unit 800 can comprise an accelerationsignal energy calculating unit, for calculating the energy of each ofthe monoaxial acceleration signals, and if the energy of each monoaxialacceleration signal is not markedly different, the step counting unit800 averages the number of the acceleration signal extreme value points,with the interfering extreme value points removed, corresponding to eachaxis, and takes the average number as the walkrun step number obtainedin this round of step counting; or, if the energy of each monoaxialacceleration signal is markedly different, the step counting unit 800determines the walkrun step number obtained in this round of stepcounting based on the number of the acceleration signal extreme valuepoints, with the interfering extreme value points removed, correspondingto the monoaxial acceleration signal with the largest energy.

Hereinbefore the step counting method and device according to thepresent invention are described with reference to the attached drawingsin an illustrative way. However, those skilled in the art shouldunderstand that, regarding the aforesaid step counting method and devicein the present invention, various modification can be made withoutdeparting from the content of the present invention. Hence, theprotection scope of the present invention should be determined by thecontent of appended claims.

It should be noted that:

Each component in the present invention can be achieved by hardware, orachieved by the software modules run on one or more processors, orachieved in a combination thereof. Those skilled in the art shouldunderstand that, some or all functions of some or all componentsaccording to the embodiments of the present invention can be achieved byusing micro-processor or digital signal processor (DSP) in practice. Thepresent invention can also be implemented as equipment or device programfor carrying out part or all of the described method herein (forexample, computer program and computer program product). The programimplementing the present invention as such can be stored on a computerreadable medium, or may be in a form of one or more signals. Such asignal can be downloaded from internet website, or provided on a carriersignal, or provided in any other forms.

For example, FIG. 8 shows a server capable of implementing the stepcounting method according to the present invention, e.g., an applicationserver. The server traditionally comprises a processor 110 and acomputer program product or computer readable medium in a form of memory120. The memory 120 can be electronic memory, such as flash memory,EEPROM (electrically erasable programmable read only memory), EPROM,hard disk, ROM or the like. The memory 120 has storage space 130 for theprogram codes 131 for performing the steps of any method in theaforesaid methods. For example, the storage space 130 for the programcodes can comprise program codes 131 respectively for implementing eachstep in the aforesaid methods. These program codes can be read/writtenfrom/into one or more computer program products. These computer programproducts comprise program code carriers such as hard disk, compact disk(CD), memory card or floppy disk and the like. Such a computer programproduct is generally a portable or fixed storage unit as shown in FIG.9. The storage unit may have storage segments, storage space and thelike similar to the memory 120 in the server of FIG. 8. The programcodes can be compressed in a suitable form. Generally, the storage unitcomprises computer readable codes 131′ for implementing the steps of themethod according to the present invention, i.e., codes that can be readby a processor such as 110, and when these codes are run by a server,each step in the aforesaid described method is carried out on theserver.

It should be noted that the aforesaid embodiments are for explaining thepresent invention rather than limiting the present invention, and thoseskilled in the art can design alternative embodiments without departingfrom the scope of appended claims. In the claims, any reference symbollocated between parentheses should not be construed as limitation to theclaims. Word “comprising” does not exclude the existence of element orstep not listed in the claims. The present invention can be implementedthrough hardware comprising various different elements and throughsuitably programmed computer. In the product claim listing variousdevices, some of these devices can be specifically implemented by onehardware item.

In the description provided herein, numerous specific details aredescribed. However, it can be understood that, the embodiments of thepresent invention can be carried out without these specific details. Insome embodiments, well-known methods, structures and techniques are notdescribed in details, so as not to blur the understanding to the presentspecification. The terms used in the present specification are mainlyselected for the purpose of readability and teaching, rather thanselected for explaining or limiting the subject matter of the presentinvention.

1. A step counting method, characterized in that, the method comprisesthe following steps performed repeatedly: obtaining three monoaxialacceleration signals with a predetermined length from triaxial output ofa triaxial acceleration sensor worn on a walkrunner; performinghigh-pass filtering on each obtained monoaxial acceleration signal;performing pitch detection on each high-pass filtered monoaxialacceleration signal, to obtain the pitch of each monoaxial accelerationsignal; selecting the lowest pitch in the three monoaxial accelerationsignals as a cut-off frequency to set a low-pass or band-pass filter,and performing low-pass or band-pass filtering on each high-passfiltered monoaxial acceleration signal by using the low-pass orband-pass filter; obtaining acceleration signal extreme value pointsfrom each low-pass or band-pass filtered monoaxial acceleration signaland removing interfering extreme value points from the accelerationsignal extreme value points; counting the number of the accelerationsignal extreme value points of each low-pass or band-pass filteredmonoaxial acceleration signal after the interfering extreme value pointshave been removed; determining the walkrun step number obtained fromthis round of step counting based on the result of counting, andcalculating the accumulative walkrun step number of the walkrunner. 2.The step counting method according to claim 1, wherein, one or moremethods of autocorrelation function method, cepstrum method, linearpredictive coding method, and average magnitude difference functionmethod are used in the pitch detection.
 3. The step counting methodaccording to claim 2, wherein, the performing pitch detection on eachhigh-pass filtered monoaxial acceleration signal comprises: performingattenuation process on each high-pass filtered monoaxial accelerationsignal by using a filter with which the attenuation of signal energyincreases from low frequency to high frequency; calculating theautocorrelation function pH of each high-pass filtered monoaxialacceleration signal after the attenuation process by using the followingformula:${\rho (\tau)} = \frac{\sum\limits_{n = 0}^{N - 1}\; {{a(n)}{a\left( {n - \tau} \right)}}}{\sqrt{\sum\limits_{n = 0}^{N - 1}\; {{a^{2}(n)}{\sum\limits_{n = 0}^{N - 1}\; {a^{2}\left( {n - \tau} \right)}}}}}$wherein, a(n) is the n-th value of each high-pass filtered monoaxialacceleration signal, N is the predetermined length of the signal, and0≦n<N, τ is a delay time, ρ(τ) is a normalized autocorrelation functionof the signal; obtaining the value of r corresponding to the maximalvalue of ρ(τ), and the reciprocal of the τ value is the pitch of thesignal.
 4. The step counting method according to claim 1, wherein, theremoving the interfering extreme value points from the accelerationsignal extreme value points comprises: filtering out the interferingextreme value points from the acceleration signal extreme value pointsthrough time gap; or, filtering out the interfering extreme value pointsfrom the acceleration signal extreme value points through time gap andmagnitude value.
 5. The step counting method according to claim 4,wherein, the interfering extreme value point comprises such anacceleration signal extreme value point that the time gap between theacceleration signal extreme value point and the previous accelerationsignal extreme value point is smaller than a predetermined threshold. 6.The step counting method according to claim 4, wherein, the interferingextreme value points comprise acceleration signal extreme value points,whose magnitude values are not maximal, among each group of accelerationsignal extreme value points in which the time gap between any twoadjacent acceleration signal extreme value points is smaller than apredetermined threshold.
 7. The step counting method according to claim1, wherein, the determining the walkrun step number obtained from thisround of step counting based on the result of counting comprises: if theenergy of each monoaxial acceleration signal not being markedlydifferent, averaging the number of the acceleration signal extreme valuepoints, with the interfering extreme value points removed, correspondingto each axis, and taking the average number as the walkrun step numberobtained in this round of step counting; or, if the energy of eachmonoaxial acceleration signal being markedly different, determining thewalkrun step number obtained in this round of step counting based on thenumber of the acceleration signal extreme value points, with theinterfering extreme value points removed, corresponding to the monoaxialacceleration signal with the largest energy.
 8. The step counting methodaccording to claim 1, further comprises: calculating displacement basedon quadratic integral of at least one monoaxial acceleration signal fortime.
 9. A step counting device, characterized in that, the devicecomprises: a triaxial acceleration sensor (100); a monoaxialacceleration signal obtaining unit (200), for obtaining three monoaxialacceleration signals with a predetermined length from the triaxialoutput of the triaxial acceleration sensor (100) worn on a walkrunner; ahigh-pass filtering unit (300), for performing high-pass filtering oneach monoaxial acceleration signal obtained from the monoaxialacceleration signal obtaining unit (200); a pitch detecting unit (400),for performing pitch detection on each high-pass filtered monoaxialacceleration signal to obtain the pitch of each monoaxial accelerationsignal; a low-pass or band-pass filtering unit (500), for selecting thelowest pitch in the three monoaxial acceleration signals as a cut-offfrequency to set a low-pass or band-pass filter, and performing low-passor band-pass filtering on each high-pass filtered monoaxial accelerationsignal by using the low-pass or band-pass filter; an extreme value pointobtaining unit (600), for obtaining acceleration signal extreme valuepoints from each low-pass or band-pass filtered monoaxial accelerationsignal and removing interfering extreme value points therein; a countingunit (700), for counting the number of the acceleration signal extremevalue points of each low-pass or band-pass filtered monoaxialacceleration signal after the interfering extreme value points have beenremoved; a step counting unit (800), for determining the walkrun stepnumber obtained from this round of step counting based on the resultcounted by the counting unit(700), and calculating the accumulativewalkrun step number of the walkrunner.
 10. The step counting deviceaccording to claim 9, wherein, the pitch detecting unit (400) comprises:an attenuation filter, for performing attenuation process on eachhigh-pass filtered monoaxial acceleration signal in such a manner thatattenuation degree increases progressively from low frequency to highfrequency; a calculating unit, for calculating an autocorrelationfunction ρ(τ) of the signal output from the attenuation filter by usingthe following formula:${\rho (\tau)} = \frac{\sum\limits_{n = 0}^{N - 1}\; {{a(n)}{a\left( {n - \tau} \right)}}}{\sqrt{\sum\limits_{n = 0}^{N - 1}\; {{a^{2}(n)}{\sum\limits_{n = 0}^{N - 1}\; {a^{2}\left( {n - \tau} \right)}}}}}$wherein, a(n) is the n-th value of the signal, N is the predeterminedlength of the signal, and 0≦n<N, τ is a delay time, pH is a normalizedautocorrelation function of the signal; a pitch obtaining unit, forobtaining the value of r corresponding to the maximal value of ρ(τ), andoutputting the reciprocal of the r value as the pitch of the high-passfiltered monoaxial acceleration signal.
 11. The step counting deviceaccording to claim 9, wherein, the counting unit (800) comprises anacceleration signal energy calculating unit, for calculating the energyof each of the monoaxial acceleration signals, and, if the energy ofeach monoaxial acceleration signal is not markedly different, the stepcounting unit (800) averages the number of the acceleration signalextreme value points, with the interfering extreme value points removed,corresponding to each axis, and takes the average number as the walkrunstep number obtained in this round of step counting; or, if the energyof each monoaxial acceleration signal is markedly different, the stepcounting unit (800) determines the walkrun step number obtained in thisround of step counting based on the number of the acceleration signalextreme value points, with the interfering extreme value points removed,corresponding to the monoaxial acceleration signal with the largestenergy.
 12. A computer program, comprising program readable codes, whenthe program readable codes are operated on a server, the serverimplements the step counting method according to claim
 1. 13. A computerreadable medium, for storing the computer program according to claim 12.